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Record W2608207285 · doi:10.3389/fnins.2017.00220

Endoplasmic Reticulum Malfunction in the Nervous System

2017· article· en· W2608207285 on OpenAlexafffund
Joanna Jung, Marek Michalak, Luis B. Agellon

Bibliographic record

VenueFrontiers in Neuroscience · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEndoplasmic Reticulum Stress and Disease
Canadian institutionsMcGill UniversityUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsEndoplasmic reticulumNeuroscienceNervous systemComputer scienceMedicineBiologyCell biology

Abstract

fetched live from OpenAlex

Neurodegenerative diseases often have multifactorial causes and are progressive diseases. Some are inherited while others are acquired, and both vary greatly in onset and severity. Impaired endoplasmic reticulum (ER) proteostasis, involving Ca2+ signaling, protein synthesis, processing, trafficking and degradation, is now recognized as a key risk factor in the pathogenesis of neurological disorders. Lipidostasis involves lipid synthesis, quality control, membrane assembly as well as sequestration of excess lipids or degradation of damaged lipids. Proteostasis and lipidostasis are maintained by interconnected pathways within the cellular reticular network, which includes the ER and Ca2+ signaling. Importantly, lipidostasis is important in the maintenance of membranes and luminal environment that enable optimal protein processing. Accumulating evidence suggest that the loss of coordinate regulation of proteostasis and lipidostasis has a direct and negative impact on the health of the nervous system.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.239
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations25
Published2017
Admission routes2
Has abstractyes

Explore more

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